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naraburns  nihil supernum  2yr ago (text post)   35888 thread views

Culture War Roundup for the week of October 3, 2022

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To me programming is quite straight forward and easy to understand. Many things that are most difficult to understand in the field come from having to use/do things that gloss over a hundred little pieces of computing reality that make things "easier." To be honest, programming is really shallow. Programming is a textual interface for controlling computers. That's it. Everything else a computer does that might be useful belongs to a different domain. And while programming is shallow per se, all those different domains that you come in contact with via programming are very deep, and that's where most of the joy comes from for me when programming. I really hate "frameworks" and programming-language fetishism. That's all fashion. The essence of form over function.

So, to answer your question, the ideal programming education in my mind starts with real programs that do real things, but that are super simple. Literally start everyone with Hello World, then Hello Susan, then Hello , etc. Read real programs that do real things, learn how they work, etc.

So how is the best way to teach data structures, algorithmic complexity, single responsibility, patterns, (more things which are not programming) etc.?

The most ubiquitous data structures and algorithms are really simple to understand. Well over half of programming with ds is sticking a bunch of primitives into a struct, giving it a name, and keeping track of a list of them (or a list of refs to them). Things like "single responsibility" and "patterns" more generally are already getting into territory that I deem to be fashion. To the extent that those ideas are useful they're trivial, and they go much beyond the point of useful in their prescriptions.

Complexity is also pretty easy to teach by just making people perform different algorithms with pen and paper so they can feel the difference between them. From that intuition it's not difficult to understand how different algos can be a better/worse choice depending on the size of your dataset. But this can also be misleading, so I would include a section on how to test these things in the real world.

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